David:
Thank you. It's perfect.
FYI: regarding your comment about "NA," yes, I filled it in just for the
example.
Again, thanks for your professional and polite reply.
Bruce
Bruce Ratner, Ph.D.
The Significant Statistician™
(516) 791-3544
Statistical Predictive Analtyics -- www.DMSTAT1.com
Machine-Learning Data Mining and Modeling -- www.GenIQ.net
David Winsemius wrote:
On Apr 2, 2017, at 11:48 AM, BR_email <b...@dmstat1.com> wrote:
Hi R'ers:
I need a jump start to obtain my objective.
Assistance is greatly appreciated.
Bruce
*******
#Given Gender Dataset
r1 <- c( 1, 2, 3)
c1 <- c( "male", "female", "NA")
GENDER <- data.frame(r1,c1)
names(d1_3) <- c("ID","Gender")
#ITYM:
names(GENDER) <- c("ID","Gender")
GENDER
--------------
_OBJECTIVE_: To dummify GENDER,
i.e., to generate two new numeric columns,
Gender_male and Gender_female,
such that:
when Gender="male" then Gender_male=1 and Gender_female=0
when Gender="female" then Gender_male=0 and Gender_female=1
when Gender="NA" then Gender_male=0 and Gender_female=0
So, with the given dataset, the resultant dataset would be as follows:
Desired Extended Gender Dataset
ID Gender Gender_male Gender_female
1 male 1 0
2 female 0 1
3 NA 0 0
With that correction I think you might want:
model.matrix( ID ~ Gender+0, data=GENDER )
Genderfemale Gendermale GenderNA
1 0 1 0
2 1 0 0
3 0 0 1
attr(,"assign")
[1] 1 1 1
attr(,"contrasts")
attr(,"contrasts")$Gender
[1] "contr.treatment"
If you assigned that to an object name, say "obj" you could get your desired
result with:
obj <- model.matrix( ID ~ Gender+0, data=GENDER )
cbind(GENDER[ , 1, drop=FALSE], obj[,-3] )
ID Genderfemale Gendermale
1 1 0 1
2 2 1 0
3 3 0 0
I get the sense that you are trying to replicate a workflow that you developed in some other language and I
think it would be more efficient for you to actually learn R rather than trying to write SAS or SPSS in R. If
you like getting "into the weeds" of the language then I suggest trying to read the code in the
`lm` function. It might help to refer back to Venables and Ripley's "S Programming" or reading
Wickham's "Advanced R" pages on the web.
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